Financial institutions are continuously making efforts to improve the products that they offer to users. Such improvements typically take one of two forms—improving the user experience (i.e., the quality, relevancy, ease of use, etc.), and improving the return for the financial institution (i.e., increasing profit, reducing risk, increasing uptake by the client, etc.). In order to make such improvements, financial institutions rely on a variety of information such as conventional statistics (e.g., daily or monthly means, maximums, and/or minimums of user accounts), job status, current debt or debt-to-income ratio, or the like. These conventional statistics are often, however, relatively mediocre predictors of user behavior, loan performance, and risk, and improved methods of predicting such factors are continuously sought by financial institutions.
Financial products are increasingly offered to users via mobile platforms, yet the mobile platform has remained largely as a conduit rather than a source of primary data for the financial institutions. Nevertheless, mobile penetration in many rural and developing areas—i.e., regions where traditional banking services are relatively less common—is quite high, and often those mobile services include mobile money accounts and mobile banking services. Financial institutions would be well served using non-traditional sources of data for offering tailor-made financial services and products.